296 citations · 580 across the 23 of their papers we have counts for
24 papers · 1 filter
FRuDA: Framework for Distributed Adversarial Domain Adaptation
Shaoduo Gan, Akhil Mathur, Anton Isopoussu +3
Breakthroughs in unsupervised domain adaptation (uDA) can help in adapting models from a label-rich source domain to unlabeled target domains. Despite these advancements, there is…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra +3
With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is gaining traction as devices become more powerful. With applications ranging from visual filte…
On-device Federated Learning with Flower
Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão +6
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do…
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris +1
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques. Typically, these models are pretrained on large G…
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
Samuel Horvath, Stefanos Laskaridis, Mario Almeida +3
Federated Learning (FL) has been gaining significant traction across different ML tasks, ranging from vision to keyboard predictions. In large-scale deployments, client heterogenei…